Generalized Network Implementations

Generalized Network Implementations
Author: John J. Jarvis
Publisher:
Total Pages: 97
Release: 1986
Genre:
ISBN:

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Generalized networks are an important class of optimization models, with uses in a wide variety of fields. This report describes the development and implementation of a generalized network algorithm. Jarvis et. al. recommend a generalized network model, system for closure optimization and planning (SCOPE), for crisis action deployment planning. In SCOPE, large generalized networks must be repeatedly solved. These networks have special structure, which results in computational advantages. In this report, a generalized network implementation is developed for solving very large generalized networks. This implementation includes new data structures for storing the basis, in-core/out-of-core handling of the arcs, and special handling of pure network structure. In this report, a detailed examination of the SCOPE model is provided and its effect on implementation issues is discussed. The SCOPE model is highly structured. This report demonstrates how this structure can be used to advantage. In a companion report extensive testing is presented which addresses the question 'what affects the computation time for a SCOPE model?' Keywords: Linear programming; Data processing; Data storage systems.

Design and Implementation of Data Structures for Generalized Networks

Design and Implementation of Data Structures for Generalized Networks
Author: Agha Iqbal Ali
Publisher:
Total Pages: 29
Release: 1984
Genre: Algorithms
ISBN:

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The specialization of the simplex algorithm for the solution of generalized network flow problems rests on the fact that a basis for the problem may be represented graphically as a spanning forest in which each component is either a one-tree or a rooted tree. The design of a specialized algorithm for efficient solution of generalized network problems necessarily depends on data structures chosen to represent the basis. This paper presents the design and detailed algorithmic specification of the primal simplex algorithm for such problems. Computational testing to determine the overhead required by generalized network data structures over pure network data structures indicates that generalized network algorithms are on the order of 2.5 to 3.5 times slower than pure network algorithms. Computational testing with generalized network problems with up to 1000 nodes and 7000 arcs establishes the suitability of the data-structures for efficient implementation of primal simplex calculations. Keywords: Linear programming. (Author).

Generalized Network Design Problems

Generalized Network Design Problems
Author: Petrica C. Pop
Publisher: Walter de Gruyter
Total Pages: 216
Release: 2012-10-30
Genre: Mathematics
ISBN: 3110267683

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Combinatorial optimization is a fascinating topic. Combinatorial optimization problems arise in a wide variety of important fields such as transportation, telecommunications, computer networking, location, planning, distribution problems, etc. Important and significant results have been obtained on the theory, algorithms and applications over the last few decades. In combinatorial optimization, many network design problems can be generalized in a natural way by considering a related problem on a clustered graph, where the original problem's feasibility constraints are expressed in terms of the clusters, i.e., node sets instead of individual nodes. This class of problems is usually referred to as generalized network design problems (GNDPs) or generalized combinatorial optimization problems. The express purpose of this monograph is to describe a series of mathematical models, methods, propositions, algorithms developed in the last years on generalized network design problems in a unified manner. The book consists of seven chapters, where in addition to an introductory chapter, the following generalized network design problems are formulated and examined: the generalized minimum spanning tree problem, the generalized traveling salesman problem, the railway traveling salesman problem, the generalized vehicle routing problem, the generalized fixed-charge network design problem and the generalized minimum vertex-biconnected network problem. The book will be useful for researchers, practitioners, and graduate students in operations research, optimization, applied mathematics and computer science. Due to the substantial practical importance of some presented problems, researchers in other areas will find this book useful, too.

An Implementation and Initial Test of Generalized Radial Basis Functions

An Implementation and Initial Test of Generalized Radial Basis Functions
Author: Dietrich Wettschereck
Publisher:
Total Pages: 80
Release: 1990
Genre: Neural networks (Computer science)
ISBN:

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Generalized Radial Basis Functions were used to construct networks that learn input-output mappings from given data. They are developed out of a theoretical framework for approximation based on regularization techniques and represent a class of three-layer networks similar to backpropagation networks with one hidden layer. A network using Gaussian base functions was implemented and applied to several domains. It was found to perform very well on the two-spirals problem and on the nettalk task. This paper explains what Generalized Radial Basis Functions are, describes the algorithm, its implementation, and the tests that have been conducted. It draws the conclusion that network. implementations using Generalized Radial Basis Functions are a successful approach for learning from examples.

Generalized Networks

Generalized Networks
Author: Richard Saeks
Publisher: Ardent Media
Total Pages: 456
Release: 1972
Genre: Technology & Engineering
ISBN: 9780030851957

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